File size: 6,808 Bytes

---
license: mit
tags:
- vla
- robotics
- multimodal
- autoregressive
library_name: transformers
pipeline_tag: robotics
---

# Being-H0: Vision-Language-Action Pretraining from Large-Scale  Human Videos

<p align="center">
    <img src="https://raw.githubusercontent.com/BeingBeyond/Being-H0/refs/heads/main/docs/assets/image/being-h0-black.png" width="300"/>
<p>

<div align="center">

[![Project Page](https://img.shields.io/badge/Website-Being--H0-green)](https://beingbeyond.github.io/Being-H0)
[![arXiv](https://img.shields.io/badge/arXiv-2507.15597-b31b1b.svg)](https://arxiv.org/abs/2507.15597)
[![Model](https://img.shields.io/badge/GitHub-Being--H0-white)](https://huggingface.co/BeingBeyond/Being-H0)
[![License](https://img.shields.io/badge/License-MIT-blue.svg)](./LICENSE)

</div>

<p align="center">
    <img src="https://raw.githubusercontent.com/BeingBeyond/Being-H0/refs/heads/main/docs/assets/image/overview.png"/>
<p>


We introduce **Being-H0**, the first dexterous Vision-Language-Action model pretrained from large-scale human videos via explicit hand motion modeling.

## News

- **[2025-08-02]**: We release the **Being-H0** codebase and pretrained models! Check our [Hugging Face Model Hub](https://huggingface.co/BeingBeyond/Being-H0) for more details. 🔥🔥🔥
- **[2025-07-21]**: We publish **Being-H0**! Check our paper [here](https://arxiv.org/abs/2507.15597). 🌟🌟🌟

## Model Checkpoints

Download pre-trained models from Hugging Face:

| Model Type | Model Name | Parameters | Description |
|------------|------------|------------|-------------|
| **Motion Model** | [Being-H0-GRVQ-8K](https://huggingface.co/BeingBeyond/Being-H0-GRVQ-8K) | - | Motion tokenizer |
| **VLA Pre-trained** | [Being-H0-1B-2508](https://huggingface.co/BeingBeyond/Being-H0-1B-2508) | 1B | Base vision-language-action model |
| **VLA Pre-trained** | [Being-H0-8B-2508](https://huggingface.co/BeingBeyond/Being-H0-8B-2508) | 8B | Base vision-language-action model |
| **VLA Pre-trained** | [Being-H0-14B-2508](https://huggingface.co/BeingBeyond/Being-H0-14B-2508) | 14B | Base vision-language-action model |
| **VLA Post-trained** | [Being-H0-8B-Align-2508](https://huggingface.co/BeingBeyond/Being-H0-8B-Align-2508) | 8B | Fine-tuned for robot alignment |

## Dataset

We have provided the dataset for post-training the VLA model. The dataset is available in Hugging Face:

| Dataset Type | Dataset Name | Description |
|--------------|--------------|-------------|
| **VLA Post-training** | [h0_post_train_db_2508](https://huggingface.co/datasets/BeingBeyond/h0_post_train_db_2508) | Post-training dataset for pretrained Being-H0 VLA model |

## Setup

### Clone repository

```bash
git clone https://github.com/BeingBeyond/Being-H0.git
cd Being-H0
```

### Create environment
```bash
conda env create -f environment.yml
conda activate beingvla
```

### Install package
```bash
pip install flash-attn --no-build-isolation
pip install git+https://github.com/lixiny/manotorch.git
pip install git+https://github.com/mattloper/chumpy.git
```

### Download MANO package

- Visit [MANO website](http://mano.is.tue.mpg.de/)
- Create an account by clicking _Sign Up_ and provide your information
- Download Models and Code (the downloaded file should have the format `mano_v*_*.zip`). Note that all code and data from this download falls under the [MANO license](http://mano.is.tue.mpg.de/license).
- Unzip and copy the contents in `mano_v*_*/` folder to the `beingvla/models/motion/mano/` folder

## Inference

### Motion Generation

- To generate hand motion tokens and render the motion, you should use the Motion Model (`Being-H0-GRVQ-8K`) and the pretrained VLA model (`Being-H0-{1B,8B,14B}-2508`). 
- You can use the following command to inference. For the `--motion_code_path`, you should use a `+` symbol to jointly specify the wrist and finger motion code paths, e.g., `--motion_code_path "/path/to/Being-H0-GRVQ-8K/wrist/+/path/to/Being-H0-GRVQ-8K/finger/"`.
- The `--hand_mode` can be set to `left`, `right`, or `both` to specify which hand to use for the task.

```bash
python -m beingvla.inference.vla_internvl_inference \
    --model_path /path/to/Being-H0-XXX \
    --motion_code_path "/path/to/Being-H0-GRVQ-8K/wrist/+/path/to/Being-H0-GRVQ-8K/finger/" \
    --input_image ./playground/unplug_airpods.jpg \
    --task_description "unplug the charging cable from the AirPods" \
    --hand_mode both \
    --num_samples 3 \
    --num_seconds 4 \
    --enable_render true \
    --gpu_device 0 \
    --output_dir ./work_dirs/
```

- **To inference on your own photos**: See [Camera Intrinsics Guide](https://github.com/BeingBeyond/Being-H0/blob/main/docs/camera_intrinsics.md) for how to estimate camera intrinsics and input them for custom inference.

### Evaluation

- You can use our pretrained VLA model to post-train on real robot data. When you get your post-trained model (e.g., `Being-H0-8B-Align-2508`), you can use the following commands to communicate with real robot, or evaluate the model on a robot task.

- Setup robot communication:

```bash
python -m beingvla.models.motion.m2m.aligner.run_server \
    --model-path /path/to/Being-H0-XXX-Align \
    --port 12305 \
    --action-chunk-length 16
```
- Run evaluation on robot task:

```bash
python -m beingvla.models.motion.m2m.aligner.eval_policy \
    --model-path /path/to/Being-H0-XXX-Align \
    --zarr-path /path/to/real-robot/data \
    --task_description "Put the little white duck into the cup." \
    --action-chunk-length 16
```

## Contributing and Building on Being-H0

We encourage researchers and practitioners to leverage Being-H0 as a foundation for their own creative experiments and applications. Whether you're adapting Being-H0 to new robotic platforms, exploring novel hand manipulation tasks, or extending the model to new domains, our modular codebase is designed to support your innovations. We welcome contributions of all kinds - from bug fixes and documentation improvements to new features and model architectures. By building on Being-H0 together, we can advance the field of dexterous vision-language-action modeling and enable robots to understand and replicate the rich complexity of human hand movements. Join us in making robotic manipulation more intuitive, capable, and accessible to all.

## Citation
If you find our work useful, please consider citing us and give a star to our repository! 🌟🌟🌟

**Being-H0**

```bibtex
@article{beingbeyond2025beingh0,
  title={Being-H0: Vision-Language-Action Pretraining from Large-Scale Human Videos},
  author={Luo, Hao and Feng, Yicheng and Zhang, Wanpeng and Zheng, Sipeng and Wang, Ye and Yuan, Haoqi and Liu, Jiazheng and Xu, Chaoyi and Jin, Qin and Lu, Zongqing},
  journal={arXiv preprint arXiv:2507.15597},
  year={2025}
}
```